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Handwriting recognition by using deep learning to extract meaningful features

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Handwriting recognition by using deep learning to extract meaningful features

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Pastor Pellicer, J.; Castro-Bleda, MJ.; España Boquera, S.; Zamora-Martinez, FJ. (2019). Handwriting recognition by using deep learning to extract meaningful features. AI Communications. 32(2):101-112. https://doi.org/10.3233/AIC-170562

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Título: Handwriting recognition by using deep learning to extract meaningful features
Autor: Pastor Pellicer, Joan Castro-Bleda, Maria Jose España Boquera, Salvador Zamora-Martinez, Francisco Julián
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] Recent improvements in deep learning techniques show that deep models can extract more meaningful data directly from raw signals than conventional parametrization techniques, making it possible to avoid specific feature ...[+]
Palabras clave: Handwriting recognition , Deep learning , Convolutional neural networks
Derechos de uso: Reserva de todos los derechos
Fuente:
AI Communications. (issn: 0921-7126 )
DOI: 10.3233/AIC-170562
Editorial:
IOS Press
Versión del editor: https://doi.org/10.3233/AIC-170562
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85854-C4-2-R/ES/AMIC-UPV: ANALISIS AFECTIVO DE INFORMACION MULTIMEDIA CON COMUNICACION INCLUSIVA Y NATURAL/
Agradecimientos:
Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R.
Tipo: Artículo

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